Development and Validation of a Pragmatic Electronic Phenotype for CKD.

Clin J Am Soc Nephrol 2019 Sep 12;14(9):1306-1314. Epub 2019 Aug 12.

Division of Renal Diseases and Hypertension, Department of Medicine, University of Minnesota, Minneapolis, Minnesota.

Background And Objectives: Poor identification of individuals with CKD is a major barrier to research and appropriate clinical management of the disease. We aimed to develop and validate a pragmatic electronic (e-) phenotype to identify patients likely to have CKD.

Design, Setting, Participants, & Measurements: The e-phenotype was developed by an expert working group and implemented among adults receiving in- or outpatient care at five healthcare organizations. To determine urine albumin (UA) dipstick cutoffs for CKD to enable use in the e-phenotype when lacking urine albumin-to-creatinine ratio (UACR), we compared same day UACR and UA results at four sites. A sample of patients, spanning no CKD to ESKD, was randomly selected at four sites for validation via blinded chart review.

Results: The CKD e-phenotype was defined as most recent eGFR <60 ml/min per 1.73 m with at least one value <60 ml/min per 1.73 m >90 days prior and/or a UACR of ≥30 mg/g in the most recent test with at least one positive value >90 days prior. Dialysis and transplant were identified using diagnosis codes. In absence of UACR, a sensitive CKD definition would consider negative UA results as normal to mildly increased (KDIGO A1), trace to 1+ as moderately increased (KDIGO A2), and ≥2+ as severely increased (KDIGO A3). Sensitivity, specificity, and diagnostic accuracy of the CKD e-phenotype were 99%, 99%, and 98%, respectively. For dialysis sensitivity was 94% and specificity was 89%. For transplant, sensitivity was 97% and specificity was 91%.

Conclusions: The CKD e-phenotype provides a pragmatic and accurate method for EHR-based identification of patients likely to have CKD.

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http://cjasn.asnjournals.org/lookup/doi/10.2215/CJN.00360119
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http://dx.doi.org/10.2215/CJN.00360119DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6730512PMC
September 2019
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References

(Supplied by CrossRef)
Development and validation of an electronic phenotyping algorithm for chronic kidney disease.
Nadkarni et al.
AMIA Annu Symp Proc 2014

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